Computer based object detection systems and methods are used in many different applications including, for example, vehicle active safety features, smart surveillance systems, and other applications. Object detection may, for example, be used in vehicle active safety features to detect, identify, and classify features in objects that are in close proximity to the vehicle. A vehicle forward collision alert (FCA) system may, for example, use an object detection system to determine if an object or person (e.g., a child, pedestrian, or other object) in front of or behind the vehicle poses a collision threat to the vehicle. An object detection system may, for example, evaluate the pose or orientation that a pedestrian is standing in to determine whether, for example, the pedestrian is moving toward the vehicle.
Object detection systems may detect objects by comparing portions of images captured with a computer imaging device such as a camera to a database of image portions in order to classify or detect objects in the image. To compare two image portions, object detection systems may calculate one or more multi-dimensional vectors, histogram representations, or other form of data representing or describing image features or parts of image features. Vectors representing each of two or more images or portions of two or more images may be compared to determine whether the images match or are similar to one another. In order to classify an image (e.g., to detect a person or object in an image), part of an image or a portion of an image, the image may be compared to a large database of images or portions of images to determine a closest match. The image comparison process may require extensive computing power depending on the size of the image and the quantity of images in the database. Faster and more efficient methods of comparing images to detect objects or features in an image may increase the functionality and efficiency of object detection systems.